Another factor affecting the accuracy of predictions is the simulated distance. Generally, errors are higher when the receiver is in the vicinity of the transmitter. For example, in [1]}, the authors concluded that for distances smaller that 500 m, the ANN presented higher errors than for larger distances. Similar findings were reported in [2]}. A workaround is to build distance-specific ML models. In [3]}, the authors created three distance-specific ANNs, for propagation distances less than 350 m, between 350 m and 700 m, and larger than 700 m. Interestingly, the ANN trained on small distances was slightly more accurate than the one trained on medium distances, due to overtraining of the first network on learning the more pronounced fast fading characteristics of smaller distances. When that network was tested on medium or large distances, it was found less accurate than the network trained on medium distances. For a distance-agnostic ML model, the higher errors observed closer to the transmitter are related to the break-point of the communication channel [4]} and the near field of the transmitting antenna, both of which depend on the frequency of operation. Generally, as frequency increases, the small-scale fading characteristics of the channel become more pronounced. That leads to increasing errors for the ML model [5]}, [6]}, [7]}, [8]}. For example, in [9]}, the authors observed increased errors at the frequency band of 3.52 GHz and 5.8 GHz compared to the one of 2.4 GHz. This is also demonstrated in the case study of the Appendix. On the other hand, in [1]}, the authors concluded that their ANN's errors did not follow a specific pattern across the frequency range used (450 MHz - 2600 MHz). However, they averaged their measurements over a 40-wavelength distance, reducing the effects of fast fading.
